Reputation analysis system and reputation analysis method

ABSTRACT

Described are a reputation analysis device, reputation analysis method, and reputation analysis-use program capable of suitably analyzing temporal changes in reputation for an object indicated by a keyword. The disclosed reputation analysis device is provided with a voluntary activity description extraction means for extracting descriptions representing voluntary activity related to an object indicated by a keyword that has been input from within a plurality of documents; and a reputation chronological data estimation means for counting the number of occurrences of voluntary activity at each time point wherein the voluntary activity expressed by a description representing the voluntary activity related to the object has been performed, and estimating reputation chronological data for chronologically representing evaluations for the object by the agents of the voluntary activity.

TECHNICAL FIELD

This invention relates to a reputation analysis system, a reputationanalysis method and a program for reputation analysis, which areutilized for analyzing change with time in reputation about a subjectindicated by an inputted keyword.

BACKGROUND ART

There are technologies which extract people's reputation about a certainsubject by analyzing a large amount of text information. Suchtechnologies are very useful for supporting people's decision making andfor marketing. For example, for persons wondering whether or not to buya certain product, it is important reference information how otherpeople evaluate the product. Further, knowing reputation about a certainproduct, enterprises can reflect it to development and promotion of asuccessive product.

In particular, technologies which analyze change in reputation with timeby analyzing the number of appearances of evaluations in the form of atime series have attracted much attention in recent years. A firstadvantage of analyzing change in reputation with time is that it ispossible to preclude evaluations of the past which are too old andineffective. A second advantage is that knowing a cause of a change inreputation becomes an important hint for decision making.

For example, it is supposed that a serious problem about a subjectbecame clear at a point of time t, and since then, reputation of thesubject changed and everyone came to have a negative evaluation of thesubject. In such a case, evaluations in days after t, where people knowthe problem, are more important hints for decision making than that indays before t, where people did not know the problem.

Also in such a case, by identifying a point of time of the reputationchange, it is possible to know the serious problem about the subjectwhich influenced an evaluation by individual person, and to use it as animportant hint for decision making.

Because of the advantages described above, technologies of extractingchange in reputation with time from a set of a large number of documentssuch as blogs have been studied in a variety of ways.

Non-patent document 1 describes a technology which extracts fromdocuments, using an evaluation-expression extraction technology,expressions used by writers of the documents when exhibiting their ownevaluations about a subject, and then sums up the numbers of appearancesof the expressions and graphs them in the form of a time-series, andthereby presents a change in reputation with time.

The technology described in non-patent document 1 firstly collectsevaluation expressions such as “good” and “bad” which are used bywriters when exhibiting their evaluations of a subject, by means ofmechanical automatic processing, and registers them in a dictionary inadvance. The technology described in non-patent document 1 limitsevaluation expressions to adjectives and adjectival verbs, and toextract such evaluation expressions, it uses a method of non-patentdocument 2.

By extracting expressions in documents which agree with expressions inthe dictionary, the technology described in non-patent document 1collects affirmative evaluation expressions and negative evaluationexpressions. The technology described in non-patent document 1 regards agraph of the numbers of appearances of affirmative evaluationexpressions and negative evaluation expressions as a graph indicatingreputation about a subject at each point of time, and outputs it.

Non-patent document 2 describes a technology, which is used innon-patent document 1, of extracting expressions which appear in anunevenly concentrated manner in affirmative reviews and negative reviewsas evaluation expressions. The technology described in non-patentdocument 2 extracts expressions, such as “bright”, “beautiful”,“terrible” and “bad”, which are often used by writers of reviews whenexhibiting their own evaluations.

Further, technologies relating to the present invention are described inpatent documents 1-3.

A technology described in patent document 1 is a search server forsearching for information used to solve a problem, which stores sub-treeinformation representing a hierarchical structure about a task includingan object word and an action word, and stores the object word and actionword and a modifier representing the problem, relating the sub-treeinformation and the group of words to each other. The search serverrecognizes an object word, an action word and a modifier from inputtedsearch words. The search server acquires stored sub-tree information onthe basis of the recognized object word, action word and modifier.

As the search server described in patent document 1 is configured asdescribed above, the user can search for information for solving aproblem by a simple and easy input, and thus can reduce the effort ofinput.

A technology described in patent document 2 is an emotion evaluationsystem, which collects text information existing on a network, thenclassifies the text information on the basis of time informationobtained along with the text information, and stores the textinformation in a storage device. From the text information, the emotionevaluation system extracts a combination of an adjective and an adverbwhich shows the sensitivity, and a noun relevant to the formers, using adictionary stored in a storage device. Further, the emotion evaluationsystem assigns, from among adjectives and adverbs relevant to each nounextracted in an emotion information extraction process, an adjective andan adverb whose appearance rates are high as indexes, and generates anemotion map corresponding to each noun, which indicates transitions ofappearance rates of adjectives and adverbs resembling respectively theadjective and the adverb assigned as indexes. The emotion evaluationsystem performs an emotion information mapping process of storing thegenerated emotion map in a storage device as an emotion map database.The emotion evaluation system performs the following emotion map searchprocess. First, when a search keyword is inputted, the emotionevaluation system searches for an emotion map resembling the most anemotion map created on the basis of a word identical with the searchkeyword from the emotion map database stored in the storage device.Secondly, the emotion evaluation system outputs the search result as apredictive result of an evaluation.

As the emotion evaluation system described in patent document 2 isconfigured as described above, the user can search for a transition ofemotion with high accuracy.

A technology described in patent document 3 is a time series informationprocessing apparatus, which detects the user's specifying input of afirst time series information as time series information in which a dateand time is related to a value. The time series information processingapparatus acquires a second time series information for comparing withthe first time series information from a database storing a plurality ofkinds of time series information. The time series information processingapparatus compares a trend of change during a predetermined period oftime in the values in the first time series information with that in thevalues in the second time series information, and calculates a degree ofresemblance between the trends of change as a correlation value by theuse of a predetermined evaluation function. When the correlation valueis equal to or larger than a predetermined threshold value, the timeseries information processing apparatus makes an order to display thefirst and the second time series information in an overlapping form onone screen.

By having the above-described configuration, the time series informationprocessing apparatus described in patent document 3 can provide atechnology which visualizes a plurality of pieces of time seriesinformation correlated with each other and thereby supports an analysis.

CITATION LIST Patent Literature

[Patent Document 1] Japanese Patent Application Laid-Open No.2005-332212

[Patent Document 2] Japanese Patent Application Laid-Open No.2007-219929

[Patent Document 3] Japanese Patent Application Laid-Open No.2008-250975

Non-Patent Literature

[Non-patent document 1] Kengo Yamana, Keisuke Nishimura, ToshihiroTakizawa, Masahide Yuasa and Minoru Ohyama, “A support system for targetselection by blog retrieval and similar target's information”,Information Processing Society of Japan SIGDD Technical Reports,2005-DD-52, pp 17-21, 2005.

[Non-patent document 2] Shigeru Fujimura, Masashi Toyoda and MasaruKitsuregawa, “Extracting evaluative expressions and reputations from theBBS”, Proceedings of the 18th Annual Conference of The Japanese Societyfor Artificial Intelligence, 3F1-03, 2004.

SUMMARY OF THE INVENTION Problem to be Solved by the Invention

However, with the methods of extracting expressions used by writers whenexhibiting their own evaluations, it is not possible to measurecontinuously and constantly change in reputation which people have withregard to a subject, and thus it is not possible to analyze change inreputation with time appropriately.

It is because expressions used by people when exhibiting their ownevaluations do not continue being exhibited continuously and constantly.Accordingly, it is not possible to correctly determine whether or not achange in reputation actually occurred on the basis of such expressions.

The present invention solves the problem described above, by focusingattention on a description of a voluntary action and thereby estimatingan evaluation held by an agent of the action. None of non-patentdocuments 1 and 2 and patent documents 1-3 described above discloses atechnology which estimates an evaluation held by an agent of an actionby discriminating between a group of words expressing voluntary actionsand a group of words expressing involuntary actions.

Therefore, the objective of the present invention is provide areputation analysis system, a reputation analysis method and a programfor reputation analysis which to solve the above-described problem andthus can appropriately analyze change with time in reputation of asubject indicated by a keyword.

Means for Solving a Problem

In order to achieve the above-mentioned objective, a reputation analysissystem of the present invention includes voluntary action descriptionextraction means which extracts, from a plurality of documents, adescription representing a voluntary action relating to a subjectindicated by an inputted keyword; and

reputation time-series data estimation means which counts the number ofappearances of voluntary actions for each point of time when a voluntaryaction expressed by a description representing a voluntary actionrelating to the subject is performed, and thereby estimating reputationtime-series data which represents evaluations of the subject by agentsof the voluntary actions in the form of a time series.

In order to achieve the above-mentioned objective, a reputation analysismethod of the present invention extracts, from a plurality of documents,a description representing a voluntary action relating to a subjectindicated by an inputted keyword; and

counts the number of appearances of voluntary actions for each point oftime when a voluntary action expressed by a description representing avoluntary action relating to the subject is performed, and therebyestimating reputation time-series data which represents evaluations ofthe subject by agents of the voluntary actions in the form of a timeseries.

In order to achieve the above-mentioned objective, a program recordingmedium storing a program for reputation analysis of the presentinvention for enabling a computer to execute the processes of

extracting, from a plurality of documents, a description representing avoluntary action relating to a subject indicated by an inputted keyword;and

counting the number of appearances of voluntary actions for each pointof time when a voluntary action expressed by a description representinga voluntary action relating to the subject is performed, and therebyestimating reputation time-series data which represents evaluations ofthe subject by agents of the voluntary actions in the form of a timeseries.

Effect of the Invention

A reputation analysis system, a reputation analysis method and a programfor reputation analysis of the present invention make it possible toappropriately analyze change with time in reputation of a subjectindicated by a keyword.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a hardware configuration diagram of a reputation analysissystem 100 according to a first exemplary embodiment of the presentinvention.

FIG. 2 is a block diagram showing a functional configuration of thereputation analysis system 100 according to the first exemplaryembodiment of the present invention.

FIG. 3 is a table showing an example of a document set stored in adocument set database 30.

FIG. 4 is a table showing examples of contents stored in a dictionary ofvoluntary action expressions 20.

FIG. 5 is a flow chart showing operation of the reputation analysissystem 100.

FIG. 6 is a table showing an example of data outputted by a voluntaryaction description extraction unit 10.

FIG. 7 is a table showing an example of reputation time-series dataestimated by a reputation time-series data estimation unit 40.

FIG. 8 is a table showing examples of contents stored in a dictionary ofvoluntary action expressions 20 in a second exemplary embodiment.

FIG. 9 is a table showing an example of data outputted by a voluntaryaction description extraction unit 10 according to the second exemplaryembodiment.

FIG. 10 is a table showing an example of reputation time-series dataestimated by a reputation time-series data estimation unit 40 accordingto the second exemplary embodiment.

FIG. 11 is a block diagram showing a functional configuration of areputation analysis system 200 according to a fourth exemplaryembodiment of the present invention.

FIG. 12 is a flow chart showing operation of the reputation analysissystem 200 according to the fourth exemplary embodiment.

FIG. 13 is a table showing an example of data outputted by a reputationchange determination unit 50 in a case where reputation time-series datais a series of scalar values.

FIG. 14 is a table showing an example of data outputted by thereputation change determination unit 50 in a case where reputationtime-series data is a series of vector values.

FIG. 15 is an explanatory drawing showing a non-transitorycomputer-readable recording medium.

EXEMPLARY EMBODIMENTS FOR CARRYING OUT OF THE INVENTION

In many of technical documents relating to the present invention, twoterms of evaluation and reputation are not clearly discriminated bytheir meanings. However, almost in general, there is a tendency todiscriminately use the two terms by calling an opinion such asaffirmation or negation which individual person has with regard to asubject an “evaluation” and by calling an overall evaluation of asociety which is obtained as a result of collecting evaluations by manypeople “reputation”. Following it, in the present specification, the twoterms are clearly discriminated by regarding an “evaluation” as thatrepresenting an opinion such as affirmation or negation which individualperson has with regard to a subject and “reputation” as thatrepresenting an overall evaluation of a society which is obtained as aresult of collecting evaluations by many people, respectively. Here,such discriminative use of the two words is merely for discriminationfor convenience to ease understanding the content, and therefore, itdoes not restrict the technical scope of the present invention.

First, in order to ease understanding the present invention, thebackground and the outline of the present invention will be described.

In blogs and the like, points of time when people intend to directlyexhibit their evaluations by themselves are unevenly concentrated atwhen new information on a subject is released. Then, at other points oftime, it is unlikely that evaluation expressions appear in a sufficientamount for an analysis of change in reputation.

For example, it is supposed that a product A has been newly put on sale.People become interested in new information on the new release of theproduct A, and various reviews such as “The product A is good in thispoint.”, “It is bad in this point.” are posted on blogs. However, aftera while, the topic loses its novelty, and the product A becomes familiarand an everyday affair, and accordingly intentional evaluations come notto be given, and thus the number of explicit evaluation expressionsdecreases. The decrease in the number of evaluation expressions is notdue to a change in reputation of the product A, but is due to merely theloss of novelty of the information.

There exists a tendency that, just after new information is obtained,humans intend to exhibit their own evaluations determined on the basisof the information. However, there also exists a tendency that, when theinformation becomes familiar after a time has passed, humans losemotivation to express evaluations and thus come not to exhibit their ownevaluations in documents. Accordingly, with methods of extractingexpressions used when exhibiting evaluations of a subject, it is notpossible to measure continuously and constantly change with time in anevaluation by individual person. In turn, also with respect toreputation which is an integral obtained by aggregating evaluations bymany people, its change with time cannot be measured continuously andconstantly. Therefore, methods of using explicit evaluation expressionsare not suitable for watching long-term change in reputation with time.

On the other hand, on determining some evaluation of a subject, as aresult of it, humans take a variety of voluntary actions reflectingcontent of the evaluation. For example, a person who determined apositive evaluation of a certain product thereafter looks for, reserves,buys or actually uses the product. If considering it conversely, it ispossible to infer that a person who looks for, reserves, buys or uses acertain product has a positive evaluation of the product.

Differing from descriptions where writers intentionally exhibit theirevaluations of a subject, descriptions of voluntary actions aredescriptions which humans use in everyday records in daily life.Accordingly, even in a period of time when humans do not intend toexplicitly describe evaluations of a subject, descriptions of voluntaryactions are used continuously and constantly. Therefore, if it ispossible to estimate people's evaluations from descriptions of voluntaryactions, continuous and constant observation of change with time ispossible also about reputation which is an integral of evaluations bymany people.

In the above-described example, even when the product A becomes familiarand an everyday affair, descriptions of voluntary actions relating tothe product A are included in a variety of people's everyday records.Accordingly, compared to descriptions of explicit evaluationexpressions, descriptions of voluntary actions are observed continuouslyand constantly. Descriptions of voluntary actions relating to theproduct A are, for example, “Today, I went out carrying the product A.”,“I cooked with the product A.” and the like.

On the basis of the above-described consideration, a reputation analysissystem of the present invention measures how people evaluate a subjectcontinuously and constantly, using descriptions of voluntary actions askeys.

In the following, exemplary embodiments of the present invention will bedescribed.

Here, with respect to the following exemplary embodiments of the presentinvention, description will be given assuming that documents subjectedto the present invention are written in the Japanese language. However,the technical scope of the present invention is not limited to thatcase. That is, even when treating documents written in languages otherthan Japanese as objects, the present invention can be applied adaptingthe present invention to grammar of each of the languages.

<First Exemplary Embodiment>

FIG. 1 is a hardware configuration diagram of a reputation analysissystem 100 according to a first exemplary embodiment of the presentinvention. As shown in FIG. 1, the reputation analysis system 100includes a CPU (central processing unit) 1, a communication interface(IF) 2, a memory 3, an HDD (hard disk drive) 4, an input device 5 and anoutput device 6. These components are connected with each other via abus 7, and they input and output data via the bus. The communication IF2 is an interface for connecting with an external network. The inputdevice 5 is, for example, a keyboard or a mouse. The output device 6 is,for example, a display or the like. The present invention is realized bythe CPU 1 executing a program stored in the memory 3 or the HDD 4.

FIG. 2 is a block diagram showing a functional configuration of thereputation analysis system 100 according to the first exemplaryembodiment of the present invention. As shown in FIG. 2, the reputationanalysis system 100 includes a voluntary action description extractionunit 10 and a reputation time-series data estimation unit 40. Further,the reputation analysis system 100 is connected with a dictionary ofvoluntary action expressions 20 and a document set database 30 via a LAN(Local Area Network) or a WAN (Wide Area Network), for example. Here,the reputation analysis system 100 may include at least either of thedictionary of voluntary action expressions 20 and the document setdatabase 30. In such a case, a dictionary of voluntary actionexpressions 20 may be realized by a storage device such as the memory 3and the HDD 4.

In the reputation analysis system 100, the voluntary action descriptionextraction unit 10 extracts, from within a plurality of documents,descriptions expressing voluntary actions relating to a subjectindicated by an inputted keyword.

The dictionary of voluntary action expressions 20 stores expressionswhich express voluntary actions (voluntary action expressions) relatingto a subject. Voluntary action expressions are expressions which areconsidered to express voluntary actions relating to a subject. In thepresent exemplary embodiment, using the dictionary of voluntary actionexpressions 20, the voluntary action description extraction unit 10extracts descriptions which include an inputted keyword and expressionsstored in the dictionary of voluntary action expressions 20 from withina plurality of documents. Using the voluntary action expressionsextracted by the use of the dictionary of voluntary action expressions20 as keys indicating evaluations of a subject, the reputationtime-series data estimation unit 40, which will be described later,estimates evaluations people have on the subject at each point of time.

The document set database 30 provides a document set (a plurality ofdocuments) which is to be a target of the extraction of descriptionsexpressing voluntary actions performed by the voluntary actiondescription extraction unit 10. FIG. 3 is a table showing an example ofa document set stored in the document set database 30. As shown in FIG.3, a document set stored in the document set database 30 includescontents and time information of documents. As specific examples ofdocuments shown in FIG. 3, mentioned are text data acquired from blogsand web pages opened to the public on the internet, e-mails and thelike. Time information is, for example, an update date attached to ablog and a web page, sending and receiving dates of an e-mail, and thelike. Time information may be that representing a time when a documentis created and also that representing a time when a document isdispatched. A unit of a point of time included in time information maybe any one of the levels of year, month, day, hour, minute and second.Here, a document set stored in the document set database 30 does notneed to be that including time information.

From descriptions expressing voluntary actions relating to a subject,the reputation time-series data estimation unit 40 estimates reputationtime-series data which represents evaluations by agents of the voluntaryactions on the subject in a time series. Specifically, on the basis ofdescriptions expressing voluntary actions extracted by the voluntaryaction description extraction unit 10, the reputation time-series dataestimation unit 40 estimates and aggregates evaluations which agents ofthe actions have on a subject and which are primary causes of thevoluntary actions. On the basis of the result, the reputationtime-series data estimation unit 40 estimates time-series data(reputation time-series data) representing reputation of a subject ateach point of time for which the voluntary action description extractionunit 10 extracted descriptions expressing voluntary actions.

For example, the reputation time-series data estimation unit 40 maygenerate time-series data of voluntary actions by counting the number ofappearances of voluntary actions for each point of time when voluntaryactions expressed by descriptions expressing voluntary actions relatingto a subject are performed. The reputation time-series data estimationunit 40 may treat the generated time-series data of voluntary actions asestimated values of reputation time-series data.

Next, description will be given in detail of the voluntary actiondescription extraction unit 10 and the dictionary of voluntary actionexpressions 20.

The voluntary action description extraction unit 10 extractsdescriptions of voluntary actions performed as results of evaluationsfrom a document set. For example, it is supposed that the followingdescription exists in a blog on the internet.

“Because product A was best in performance and low in price, I bought aproduct A yesterday.”

In the above-mentioned description, “bought” is a voluntary actionperformed as a result of an evaluation. The previous technologiesrelating to the present invention have extracted descriptions “good inperformance” and “low in price”, which are expressions used toexplicitly exhibit evaluations of a subject as evaluation expressions.In contrast to that, the voluntary action description extraction unit 10of the present invention extracts a description “bought a product Ayesterday.” Further, the configuration may be such that descriptions ofvoluntary actions are extracted in addition to evaluation expressionswhich alone have been extracted in the relating technologies.

The voluntary action description extraction unit 10 does not extractdescriptions of involuntary actions. It is because, differing from thatof voluntary actions, descriptions of involuntary actions cannot be usedfor estimating evaluations held by agents of the actions. For example, adescription “I saw a product A in an electric appliance storeyesterday.” is supposed to exist. In this case, because an action of“see” occurs not depending on intention of an agent of the action, itcannot be a key to estimate what kind of evaluation the agent of theaction “I” has on the subject. Alternatively, a description “I went tosee a product A in an electric appliance store yesterday.” is supposedto exist. In this case, an action of “went to see” is a voluntary actionreflecting intention of an agent of the action, and it is possible todetermine that the agent of the action has a positive evaluation on, orat least an interest in, the product A. Therefore, in order to use itfor estimating an evaluation of the product A held by the agent of theaction, the voluntary action description extraction unit 10 extracts thedescription “went to see a product A”.

Accordingly, the voluntary action description extraction unit 10extracts only descriptions of voluntary actions which agents of theactions intentionally performs, such as “buy a product A”, “use aproduct A”, “listen to music on a product A”, “go to see a facility A”,“check about a service A” and “reserve a service A”. The voluntaryaction description extraction unit 10 does not extract passivedescriptions of involuntary actions such as “see a product A”, “be givena ticket of a facility A” and “know a start of a service A”.

FIG. 4 is a table showing examples of contents stored in the dictionaryof voluntary action expressions 20. As shown in FIG. 4, it is onlynecessary for the dictionary of voluntary action expressions 20 at leastto list voluntary action expressions separately. The simplest examplesof voluntary action expressions are verbs expressing voluntary actionsrelating to a subject, among the words grammatically classified asverbs. As such verbs, “buy”, “go” and the like are considered. Thedictionary of voluntary action expressions 20 may be a dictionarystoring such verbs expressing voluntary actions.

The dictionary of voluntary action expressions 20 may also storeexpressions each composed of a combination of a plurality of words whichare considered to express voluntary actions relating to a subject. Forexample, “go to see”, “make a reservation”, “buy a ticket” and the likeare such expressions.

Here, not all of voluntary actions relating to a subject can be keys forestimating evaluations of the subject by agents of the actions. Forexample, an expression “moved (a subject)” can be said to express avoluntary action relating to a subject, but it is difficult to estimatewhat kind of evaluation an agent of the action had on the subject fromthe fact that the agent only moved the subject. Therefore, withoutstoring such voluntary action expressions difficult to use as keys ofevaluation estimation from the beginning, the dictionary of voluntaryaction expressions 20 may stick to storing only expressions of voluntaryactions performed as results of evaluations.

As voluntary actions performed as results of evaluations, actions toutilize a subject, actions with intention to utilize a subject oractions to enable utilization of a subject are representative ones.

Actions to utilize a subject correspond to, for example, suchexpressions as “buy”, “use” and “do something with (a subject)”. Actionswith intention to utilize a subject correspond to, for example, suchexpressions as “look for” and “check about (a subject)”. Actions toenable utilization of a subject correspond to, for example, suchexpressions as “install” and “reserve”.

Next, operation of the reputation analysis system 100 will be described.

FIG. 5 is a flow chart showing operation of the reputation analysissystem 100. As shown in FIG. 5, as a first step, the voluntary actiondescription extraction unit 10 accepts an input of a keyword from theuser (S1). Next, the voluntary action description extraction unit 10extracts descriptions of voluntary actions from a document set (S2).Then, the reputation time-series data estimation unit 40 estimatesreputation time-series data (S3).

Now, operation of S1 will be described specifically. In the presentexemplary embodiment, the user performs an input of a keyword to thevoluntary action description extraction unit 10 using the input device 5which is a keyboard or the like. Alternatively, the user may perform aninput of a keyword to the voluntary action description extraction unit10 by means of an external computer connected by a network viacommunication IF 2. There is no particular restriction of a keyword,and, as examples of keywords, mentioned are an object, a service, anorganization, an event or the like about which the user would like todetermine change in evaluations with time. Further, the number ofkeywords to be inputted may be one, and also may be more than one if allof the inputted keywords express an identical object, service or thelike.

In the way described above, the voluntary action description extractionunit 10 accepts the user's inputting a keyword by means of a keyboard orthe like.

Next, operation of S2 will be described specifically. In the presentexemplary embodiment, using the dictionary of voluntary actionexpressions 20, the voluntary action description extraction unit 10extracts descriptions including a keyword inputted from the outside andvoluntary action expressions registered in the dictionary of voluntaryaction expressions 20 from a document set.

Specifically, when a keyword is inputted, the voluntary actiondescription extraction unit 10 searches a document set in the documentset database 30 and acquires a document set including the inputtedkeyword. Then, from within the acquired document set, the voluntaryaction description extraction unit 10 extracts voluntary actionexpressions which the dictionary of voluntary action expressions 20stores. Documents from which these expressions were extracted includedescriptions including the above-mentioned keyword and voluntary actionexpressions. The voluntary action description extraction unit 10 mayoutput the descriptions including the keyword and the voluntary actionexpressions to the reputation time-series data estimation unit 40,regarding the descriptions themselves as voluntary action descriptions(descriptions in which voluntary action expressions are included) whichare given based on evaluations of a subject.

FIG. 6 is a table showing an example of data outputted by the voluntaryaction description extraction unit 10. As shown in FIG. 6, the voluntaryaction description extraction unit 10 outputs data including voluntaryaction expressions and time information to the reputation time-seriesdata estimation unit 40. Here, when an expression in terms of time isincluded in a description including a voluntary action expression,output data corresponding to the description does not need to includetime information. Details about a description in terms of time will bedescribed later.

Next, operation of S3 will be described specifically. The reputationtime-series data estimation unit 40 receives the data includingvoluntary action expressions outputted by the voluntary actiondescription extraction unit 10, and on the basis of the data, itestimates time-series data which represents reputation of the subject ateach point of time. The reputation time-series data outputted by thereputation time-series data estimation unit 40 is a bunch of data wherevalues each representing a magnitude of reputation at a point of timeare put into a group as a time series. The value representing amagnitude at each point of time may be a scalar value.

FIG. 7 is a table showing an example of reputation time-series dataestimated by the reputation time-series data estimation unit 40. Asshown in FIG. 7, reputation time-series data may be only one series ofscalar values in terms of time.

Now, description will be given of a method by which the reputationtime-series data estimation unit 40 estimates reputation time-seriesdata from voluntary action descriptions, in the present exemplaryembodiment. The reputation time-series data estimation unit 40 countsthe number of appearances of voluntary actions for each point of timewhen voluntary actions expressed by descriptions expressing thevoluntary actions were performed, and assigns the counting result asestimated values of reputation time-series data. That is, the reputationtime-series data estimation unit 40 estimates a point of time when avoluntary action written in a voluntary action description was performedto be a point of time when an evaluation of a subject was given by anagent of the action. By counting the number of voluntary actions at eachpoint of time when voluntary actions were performed, the reputationtime-series data estimation unit 40 estimates a magnitude of reputationat each point of time.

The reputation time-series data estimation unit 40 may be configured ina manner where it estimates a point of time when a voluntary action wasperformed using at least either of time information indicating a time ofcreation or dispatching of a document and an expression in terms of timewhich is given in a description expressing the voluntary action.

That is, the reputation time-series data estimation unit 40 may estimatea point of time indicated by time information of a document including avoluntary action description to be a point of time when the voluntaryaction is performed. Further, the reputation time-series data estimationunit 40 may estimate a point of time when a voluntary action wasperformed using not only time information of a document but also anexpression in terms of time given in the description. For example, it issupposed that a voluntary action description “I cooked using a product Ayesterday.” is included in a document accompanied with time informationindicating a dispatching date of Apr. 5, 2005. In this case, thereputation time-series data estimation unit 40 may estimate that thevoluntary action was performed on the previous day of the documentdispatching, that is, Apr. 4, 2005.

Further, when time information is not attached but an expression interms of time is included in a description including a voluntary actionexpression, the reputation time-series data estimation unit 40 mayestimate from the expression a point of time when the voluntary actionwas performed. For example, it is supposed that a description “I cookedusing a product A on Apr. 4, 2005.” is given in a document whosedispatching date is unknown. In this case, using the expression “Apr. 4,2005”, which is an expression in terms of time, the reputationtime-series data estimation unit 40 may estimate a point of time whenthe voluntary actions “use” and “cook” were performed to be Apr. 4,2005.

By the way described above, the reputation time-series data estimationunit 40 estimates points of time when voluntary actions were performed,and then counts the number of appearances of voluntary actions at eachpoint of time. On the basis of this number of appearances of voluntaryactions, the reputation time-series data estimation unit 40 estimates amagnitude of reputation at each point of time.

For example, the reputation time-series data estimation unit 40 mayregard the number of appearances of voluntary actions at each point oftime, itself, as an estimated value representing a magnitude ofreputation at each point of time, and thereby generate reputationtime-series data in the form of a series of scalar values such as shownin FIG. 7. In this case, the reputation time-series data estimation unit40 calculates a total of the numbers of appearances of all kinds ofvoluntary actions, regardless of what kind of evaluation each voluntaryaction specifically represents. That many people took voluntary actionson a subject indicates that many people were interested in the subject.Therefore, the present reputation time-series data is the one where atransition with time of a magnitude of people's interest is particularlyshown in the form of a series of scalar values.

Any program which enables a computer to execute the steps S1-S3 shown inFIG. 5 may be used as a program for reputation analysis of the presentexemplary embodiment. By installing the program in a computer andexecuting it, the user realizes the reputation analysis system 100 andthe reputation analysis method. In this case, the CPU 1 of a computershown in FIG. 1 functions as the voluntary action description extractionunit 10 and the reputation time-series data estimation unit 40 andexecutes the processes.

As has been described above, according to the reputation analysis system100 according to the first exemplary embodiment, even when the number ofexpressions used to exhibit own evaluations, change with time inreputation of a subject indicated by a keyword can be analyzedappropriately.

It is because the reputation analysis system 100 does not extractdescriptions directly exhibiting evaluations of a subject indicated by akeyword as evaluation expressions, but does focus attention on andextract descriptions expressing voluntary actions performed relating tothe subject. Because of the extraction of descriptions expressingvoluntary actions, the reputation analysis system 100 can measuresufficient number of values to determine a change in reputation morecontinuously and more constantly than the methods of extractingdescriptions directly exhibiting evaluations.

<Second Exemplary Embodiment>

A reputation analysis system 100 according to a second exemplaryembodiment of the present invention is different from the reputationanalysis system 100 according to the first exemplary embodiment in thatit uses a dictionary of voluntary action expressions 20 further storingtypes of evaluations of a subject by agents of voluntary actions whichcan be estimated from voluntary actions expressed by voluntary actionexpressions.

FIG. 8 is a table showing examples of contents stored in the dictionaryof voluntary action expressions 20 in the second exemplary embodiment.As shown in FIG. 8, the dictionary of voluntary action expressions 20stores, in addition to expressions which express voluntary actionsrelating to a subject, evaluation types of evaluations of the subject byagents of the voluntary actions which can be estimated from thevoluntary actions expressed by the expressions. For example, thedictionary of voluntary action expressions 20 stores “buy” as avoluntary action expression, and stores also an evaluation type“positive” corresponding to the expression, in a pair with theexpression. Accordingly, if a voluntary action of “buy” is written in adocument relating to a subject, the reputation analysis system 100estimates, using the dictionary of voluntary action expressions 20, thatthe agent of the action who performed an action of “buy” has a“positive” evaluation of the subject. Specifically, when there is adescription “I bought a product A today.”, the reputation analysissystem 100 estimates that an agent of the action “buy” had a “positive”evaluation of the “product A”.

The dictionary of voluntary action expressions 20 stores, for example,“positive”, “negative”, “neutral” and the like, as evaluation types.Also, the dictionary of voluntary action expressions 20 may store thepresence or absence of an interest as an evaluation type. For example, avoluntary action “I checked about (a subject).” shows an interest in thesubject.

Further, the dictionary of voluntary action expressions 20 may storeoptional evaluation types expressing evaluations of a subject by agentsof actions, such as “be interested in”, “using in daily life” and “thinkunnecessary”. It is for enabling the reputation analysis system 100 toanalyze evaluations of a subject held by agents of actions from a largervariety of viewpoints. For example, with the dictionary of voluntaryaction expressions 20 storing information about whether “be interestedin” or “be not interested in” as a pair of voluntary action expressions,the reputation analysis system 100 may determine evaluations of asubject by agents of actions dividing them into positive evaluations andnegative ones.

With respect to the first exemplary embodiment, it was mentioned thatactions to utilize a subject, actions with intentions to utilize asubject and actions to enable utilization of a subject arerepresentatives of voluntary actions performed as results ofevaluations. The dictionary of voluntary action expressions 20 accordingto the second exemplary embodiment may store negative evaluations inpairs with actions not to utilize a subject, actions with intentions notto utilize a subject or actions to disable utilization of a subject.

Actions not to utilize a subject correspond to, for example, expressionssuch as “do not buy”, “do not use” and “quit”. Actions with intentionsnot to use correspond to, for example, expressions such as “stow” and“clear up”. Actions to disable utilization correspond to, for example,expressions such as “discard” and “uninstall”.

In the second exemplary embodiment, using the dictionary of voluntaryaction expressions 20, the voluntary action description extraction unit10 extracts descriptions of voluntary actions from a document set, andoutputs data including evaluation types corresponding to voluntaryaction expressions included in the descriptions of voluntary actions toa reputation time-series data estimation unit 40.

FIG. 9 is a table showing an example of data outputted by the voluntaryaction description extraction unit 10 according to the second exemplaryembodiment. As shown in FIG. 9, the voluntary action descriptionextraction unit 10 according to the present exemplary embodiment outputsdata including voluntary action expressions, time information andevaluation types to the reputation time-series data estimation unit 40.

In the second exemplary embodiment, the reputation time-series dataestimation unit 40 receives data including evaluation typescorresponding to voluntary action expressions from the voluntary actiondescription extraction unit 10. The reputation time-series dataestimation unit 40 estimates reputation time-series data on the basis ofvector values which are calculated by counting the number of appearancesof evaluations of a subject by agents of voluntary actions which areestimated from expressions expressing the voluntary actions, for eachtype of evaluation.

FIG. 10 is a table showing an example of reputation time-series dataestimated by the reputation time-series data estimation unit 40according to the second exemplary embodiment. As shown in FIG. 10, thereputation time-series data estimation unit 40 according to the secondexemplary embodiment estimates reputation time-series data in the formof a series of vector values whose elements represent respectivemagnitudes of reputation of “positive” and “negative”.

That is, the reputation time-series data estimation unit 40 estimatesreputation time-series data in the form of a series of vector valuessuch as shown in FIG. 10, by counting the number of appearances ofvoluntary action descriptions at each point of time when the voluntaryactions were performed, separately for each evaluation type ofevaluations corresponding to the voluntary actions. For example, whenconsidering two kinds of evaluation types, the reputation time-seriesdata estimation unit 40 counts and sums up separately the number ofappearances of voluntary action descriptions from which positiveevaluations are estimated and that of voluntary action descriptions fromwhich negative evaluations are estimated. The reputation time-seriesdata estimation unit 40 estimates reputation time-series data composedof vector values whose elements are two values respectively representinga magnitude of positive reputation and that of negative reputation.

Now, description will be given of operation of the reputation analysissystem 100 according to the second exemplary embodiment.

Operation of the reputation analysis system 100 according to the secondexemplary embodiment is shown by FIG. 5 similarly to that of thereputation analysis system 100 according to the first exemplaryembodiment.

However, operation of the reputation analysis system 100 according tothe second exemplary embodiment is different from that of the reputationanalysis system 100 according to the first exemplary embodiment in thatit uses the dictionary of voluntary action expressions 20 furtherstoring types of evaluations of a subject in the step S2 of extractingvoluntary action descriptions. Further, operation of the reputationanalysis system 100 according to the second exemplary embodiment isdifferent from that of the reputation analysis system 100 according tothe first exemplary embodiment also in that it counts the number ofappearances of voluntary action descriptions separately for eachevaluation type of evaluations corresponding to the voluntary actions inthe step S3 of estimating reputation time-series data.

The step S3 is the same as that in operation of the reputation analysissystem 100 according to the first exemplary embodiment, and thereforeits description is omitted.

Now, operation of S2 will be described specifically. Using thedictionary of voluntary action expressions 20, the voluntary actiondescription extraction unit 10 extracts, from a document set,descriptions including a keyword inputted from the outside and voluntaryaction expressions registered in the dictionary of voluntary actionexpressions 20. The dictionary of voluntary action expressions 20 in thesecond exemplary embodiment further stores evaluations of a subject byagents of voluntary actions which can be estimated from the voluntaryactions expressed by voluntary action expressions.

When a keyword is inputted, the voluntary action description extractionunit 10 searches a document set in the document set database 30 andacquires a document set including the inputted keyword. Then, fromwithin the acquired document set, the voluntary action descriptionextraction unit 10 extracts voluntary action expressions which thedictionary of voluntary action expressions 20 stores. Documents fromwhich these expressions were extracted include descriptions includingthe above-mentioned keyword and voluntary action expressions. Thevoluntary action description extraction unit 10 outputs data (forexample, FIG. 9) including the voluntary action descriptions whichincludes the keyword and voluntary action expressions, and evaluationtypes corresponding to the voluntary action expressions to thereputation time-series data estimation unit 40.

Next, operation of S3 will be described specifically. The reputationtime-series data estimation unit 40 receives the data includingvoluntary action expressions outputted by the voluntary actiondescription extraction unit 10, and on the basis of the data, itestimates time-series data representing reputation of the subject ateach point of time, for each evaluation type. The reputation time-seriesdata outputted by the reputation time-series data estimation unit 40 isa bunch of data where values each representing a magnitude of reputationat respective points of time are put into a group. In the presentexemplary embodiment, the values each representing a magnitude ofreputation at respective points of time are each vector values.

The reputation time-series data estimation unit 40 estimates reputationtime-series data, such as shown in FIG. 10, which is represented as aseries of vector values each representing a magnitude of reputation foreach evaluation type

A program for reputation analysis in the present exemplary embodiment isa program which enables a computer to execute the steps S1-S3 shown inFIG. 5, and may be any program enabling execution of the above-describedoperation.

As has been described above, according to the second exemplaryembodiment of the present invention, it is possible to estimateevaluations which agents of actions have on a subject from a largervariety viewpoints. It is because the dictionary of voluntary actionexpressions 20 stores types of evaluations considered as primary causesof a voluntary action expression in a pair with the voluntary actionexpression.

Further, according to the second exemplary embodiment of the presentinvention, because of the estimation of reputation time-series data foreach evaluation type, change in evaluations of a subject can be analyzedin more detail. For example, according to the second exemplaryembodiment of the present invention, possible is an analysis such asshowing that positive evaluations are the same as before and onlynegative evaluations greatly increased.

<Third Exemplary Embodiment>

A reputation analysis system 100 according to the third exemplaryembodiment of the present invention is different from the reputationanalysis system 100 according to the first exemplary embodiment in thata voluntary action description extraction unit 10 of it extractsvoluntary action descriptions on the basis of a regulation whichprescribes positional and grammatical relations between a keywordrepresenting a subject and a voluntary action expression.

For example, the voluntary action description extraction unit 10 mayextract a voluntary action description when a voluntary actionexpression and a keyword representing a subject collocate with eachother within a distance of N words in a sentence of the voluntary actiondescription. Alternatively, the voluntary action description extractionunit 10 may extract a voluntary action description when a voluntaryaction expression and a keyword representing a subject are used there ina relation with each other which corresponds to a relation of theWO-case or the DE-case in Japanese grammar, such as “(a subject) WOTUKAU/use (a subject)” and “(a subject) DE ONGAKU WO KIKU/listen tomusic by (a subject)”. Here, the relations corresponding to relations ofthe WO-case and the DE-case in Japanese Grammar are, respectively, arelation showing that between an action (an expression expressed by averb, and the like) and an object of the action, and a relation showingthat between an action and a means for the action (a tool and a methodfor an action, and the like). This kind of regulation on relation isapplied not only to cases in Japanese language, but also may be appliedto any cases where there is a relation between a voluntary actionexpression and a keyword representing a subject corresponding to arelation between an action and an object of the action or that betweenan action and a means for the action, in a language of a document whichis to be a processing target when the present invention is applied.

Alternatively, a configuration may be such that the dictionary ofvoluntary action expressions 20 stores a regulation on positional orgrammatical relations between a keyword and voluntary actionexpressions, relating the regulation to the voluntary actionexpressions, and the voluntary action description extraction unit 10extracts only voluntary action descriptions satisfying the regulation.

The voluntary action description extraction unit 10 may extractvoluntary action descriptions according to regulations determined byinflection and dependent words, and those expressed by them such aspolarity (whether positive or negative form), voice, aspect, tense, moodand modality.

The voluntary action description extraction unit 10 may extractvoluntary action descriptions considering evaluation types as in thesecond exemplary embodiment and accordingly determining an evaluationtype as positive for descriptions whose polarity is positive and asnegative for descriptions whose polarity is negative. Specifically, thevoluntary action description extraction unit 10 extracts descriptionswith positive polarity such as “I bought (a subject).” determining theirevaluation types as positive. Further, the voluntary action descriptionextraction unit 10 extracts descriptions with negative polarity such as“I did not buy (a subject).” determining their evaluation types asnegative.

Further, even when expressions do not express actually performed actionsbut do situations where objectives have not been accomplished, such as“intended to utilize”, “intended to buy” and “intended to use”, thevoluntary action description extraction unit 10 may extract themregarding them as voluntary action expressions. For example, adescription such as “Today, I intended to utilize a facility A, butcould not, because it was closed.” is supposed to exist. The voluntaryaction description extraction unit 10 may extract this voluntary actiondescription, determining that an agent of the action to intend toutilize the facility A had a positive evaluation of the facility A evenif the agent could not utilize it actually.

On the other hand, the voluntary action description extraction unit 10does not need to extract actions performed under enforcement from theoutside and actions reluctant to the agents, not regarding them asvoluntary action expressions, even if they are actions usually regardedas voluntary actions. For example, the voluntary action descriptionextraction unit 10 may extract descriptions according to a regulationsuch as that where an expression “I used (a subject)” is regarded as avoluntary action but expressions “I enforced to use (a subject)” and “Iunintentionally used (a subject)” are not regarded as voluntary actions.

The voluntary action description extraction unit 10 may extractvoluntary action descriptions according to a regulation on an agent ofaction. For example, while the voluntary action description extractionunit 10 extracts a description “Yesterday, I bought (a subject)”, itdoes not need to extract a description “Yesterday, some star said thathe bought (a subject)”. It is because, in the case of the description“Yesterday, I bought (a subject)”, the agent of the action “bought” isthe writer of the document. Therefore, the action “bought” can be saidto be an action reflecting an evaluation by the writer of the document.In contrast to it, in the case of the description “Yesterday, some starsaid that he bought (a subject).” the agent of the action is the star.Therefore, the action “bought” in this case can be said to be an actionreflecting an evaluation by the star. Even if descriptions written by alarge number of people telling about an identical action by the star arecollected, only an evaluation by that one star can be known, but no keysfor knowing evaluations by the large number of people themselves can beobtained from the descriptions. Therefore, the voluntary actiondescription extraction unit 10 may extract descriptions according to aregulation that a description is extracted only when the agent of theaction expressed in the voluntary action description is the writer ofthe document.

Besides, the voluntary action description extraction unit 10 can extractvoluntary action descriptions more precisely, according to regulationsprescribing a relation between a voluntary action expression and akeyword representing a subject in a variety of ways.

Now, description will be given of operation of the reputation analysissystem 100 according to the third exemplary embodiment.

Operation of the reputation analysis system 100 according to the thirdexemplary embodiment is shown by FIG. 5, similarly to the reputationanalysis system 100 according to the first exemplary embodiment.

However, in the operation of the reputation analysis system 100according to the third exemplary embodiment, operation of the step S2 ofextracting voluntary action descriptions is different from that in thereputation analysis system 100 according to the first exemplaryembodiment. That is, in the present exemplary embodiment, the voluntaryaction description extraction unit 10 extracts voluntary actiondescriptions according to a regulation which prescribes positional orgrammatical relations between a keyword representing a subject and avoluntary action expression.

Here, the steps S1 and S3 are the same as that of the reputationanalysis system 100 according to the first exemplary embodiment, andtherefore their descriptions are omitted.

Now, operation of S2 will be described specifically. The voluntaryaction description extraction unit 10 may extract a voluntary actiondescription when a voluntary action expression and a keywordrepresenting a subject collocate with each other within a distance of Nwords in a sentence of the voluntary action description. Alternatively,the voluntary action description extraction unit 10 may extract avoluntary action description when a voluntary action expression and akeyword representing a subject are used there in a relation with eachother which corresponds to a relation of the WO-case or the DE-case inJapanese grammar, such as “(a subject) WO TUKAU/use (a subject)” and “(asubject) DE ONGAKU WO KIKU/listen to music by (a subject)”. Here, therelations corresponding to relations of the WO-case and the DE-case inJapanese Grammar are, respectively, a relation showing that between anaction (an expression expressed by a verb, and the like) and an objectof the action, and a relation showing that between an action and a meansfor the action (a tool and a method for an action, and the like).

Besides, the voluntary action description extraction unit 10 extractsvoluntary action descriptions according to the variety of regulationsalready described above.

A program for reputation analysis in the present exemplary embodiment isa program which enables a computer to execute the steps S1-S3 shown inFIG. 5, and may be any program enabling execution of the above-describedoperation.

As has been described above, according to the third exemplary embodimentof the present invention, evaluations held by agents of actions can beestimated more precisely. It is because the voluntary action descriptionextraction unit 10 extracts voluntary action descriptions according to aregulation determined by positional or grammatical relations between akeyword representing a subject and a voluntary action expression.

<Fourth Exemplary Embodiment>

FIG. 11 is a block diagram showing a functional configuration of areputation analysis system 200 according to a fourth exemplaryembodiment of the present invention. As shown in FIG. 11, the reputationanalysis system 200 according to the fourth exemplary embodiment of thepresent invention is different from the reputation analysis system 100in that it further comprises a reputation change determination unit 50.

A voluntary action description extraction unit 10, a dictionary ofvoluntary action expressions 20, a document set database 30 and areputation time-series data estimation unit 40 are the same as thosedescribed with respect to the first exemplary embodiment, and thereforetheir descriptions are omitted.

The reputation change determination unit 50 determines change with timein reputation of a subject, on the basis of an estimation result by thereputation time-series data estimation unit 40. The reputation changedetermination unit 50 outputs the estimated change with time inreputation of a subject as visualized data to, for example, the outputdevice 6.

On the basis of reputation time-series data outputted by the reputationtime-series data estimation unit 40, the reputation change determinationunit 50 determines the amount of change in reputation around a specifiedpoint of time t. Here, a specific point of time t is present within atime range from the oldest point of time to the newest point of timetreated in reputation time-series data. The reputation changedetermination unit 50 determines a change in reputation around the pointof time t by determining the amount of change in a magnitude of thereputation, setting t as a reference point. The reputation changedetermination unit 50 may generate time-series data of the amount ofchange in reputation, by setting a plurality of t's and therebycalculating the amounts of change at the plurality of points of time.

There are a variety of ways to define specified points of time t's. Thereputation change determination unit 50 may determine a change inreputation for every point of time included in reputation time-seriesdata. Alternatively, the reputation change determination unit 50 maydefine t's by randomly sampling optional points of time in a range fromthe oldest point of time to the newest point of time treated inreputation time-series data. Further, the user may register in advance aparticular point of time, such as a date and time when an event relatingto a subject occurred, as t in the reputation change determination unit50.

Now, operation of the reputation analysis system 200 according to thefourth exemplary embodiment will be described.

FIG. 12 is a flow chart showing operation of the reputation analysissystem 200 according to the fourth exemplary embodiment. As shown inFIG. 12, the operation of the reputation analysis system 200 isdifferent from the operation of the reputation analysis system 100according to the first exemplary embodiment (FIG. 5) in that it includesa step S4 of determining the amount of change in reputation.

The steps S1-S3 are the same as those of the reputation analysis system100 according to the first exemplary embodiment, and therefore theirdescriptions are omitted.

In S4, the reputation change determination unit 50 determines the amountof change in reputation. The reputation change determination unit 50determines the amount of change in reputation at a point of time t bysetting t as a reference point and comparing reputation time-series datafor a period before t with reputation time-series data for a periodafter t.

Now, specific description will be given of operation of the reputationchange determination unit 50 when reputation time-series data outputtedby the reputation time-series data estimation unit 40 is a series ofscalar values. With respect to a point of time t within a time rangesubjected to the time-series data, the reputation change determinationunit 50 sums up values in reputation time-series data about each ofperiods of a few days before t and of a few days after t, with t set asa reference point of time. The reputation change determination unit 50calculates a ratio between the summed values each about the past and thefuture, and regards the obtained ratio as the amount of change at thepoint of time t.

FIG. 13 is a table showing an example of data outputted by thereputation change determination unit 50 when reputation time-series datais a series of scalar values. As shown in FIG. 13, the reputation changedetermination unit 50 may output data where time information and theamount of change at each point of time subjected to the determinationare combined into a pair. The data in FIG. 13 is that obtained bysumming up values (estimated values in FIG. 7) of preceding two days ofa reference point of time t and values of subsequent two days of thetime t, respectively, and then calculating a ratio between the summedvalues. Columns where “−” are given are of periods on whichdetermination was not performed because data was insufficient for eitherthe past or the future.

When reputation time-series data outputted by the reputation time-seriesdata estimation unit 40 is a series of vector values, the reputationchange determination unit 50 calculates the amount of change at eachpoint of time t, in a similar way to the case of scalar values, for eachof elements constituting a vector value. The reputation changedetermination unit 50 outputs time-series data representing the amountof change in reputation for each evaluation type.

FIG. 14 is a table showing an example of data outputted by thereputation change determination unit 50 when reputation time-series datais a series of vector values. As shown in FIG. 14, when reputationtime-series data is a series of vector values each composed of twoelements representing positive reputation and negative reputationrespectively, the reputation change determination unit 50 calculatesseparately the amount of change in the value of the element representingpositive reputation and that in the value of the element representingnegative reputation, at a point of time t. The reputation changedetermination unit 50 outputs a series of vector values each composed oftwo elements which are the amounts of changes at a point of timerespectively in the value representing positive reputation and in thevalue representing negative reputation. Values of each element of thevector of the output example in FIG. 14 are that obtained, similarly tothe case of FIG. 13, by summing up values (values of each vector elementin FIG. 10) of preceding two days and that of subsequent two days,respectively, and then calculating a ratio between the summed values.

Further, instead of considering the amount of change separately withrespect to each element constituting a vector value, the reputationchange determination unit 50 may calculate the amount of change in acombination of a plurality of elements. For example, the reputationchange determination unit 50 may calculate a sum of values of aplurality of elements at each point of time to produce a time series,and then calculate the amount of change with respect to the series ofsums. Further, the reputation change determination unit 50 may calculatea ratio between a plurality of elements at each point of time to producea time series, and then calculate the amount of change with respect tothe series of ratios. The reputation change determination unit 50 maycalculate the amount of change with respect to these series similarly tocalculating the amount of change with respect to scalar values.

A program for reputation analysis in the present exemplary embodimentmay be any program which enables a computer to execute the steps S1-S4shown in FIG. 12.

As has been described above, according to the fourth exemplaryembodiment of the present invention, change with time in reputation of asubject indicated by a keyword can be determined at a glance. It isbecause the reputation change determination unit 50 outputs the amountsof change in reputation.

Here, a program of each of the first to the fourth exemplary embodimentsdescribed above may be such that a recording medium 8 which recordscodes of the program is provided in a computer which realizes areputation analysis system, and a CPU 1 retrieves and executes the codesof the program stored in the recording medium 8. Alternatively, a CPU 1may store (install) the codes of the program stored in the recordingmedium 8 into either or both of a memory 3 and an HDD 4. That is, thefirst to the fourth exemplary embodiments described above include theexemplary embodiment of a recording medium 8 which stores temporarily orpermanently a program (software) to be executed by a computer (CPU 1).

While the invention has been particularly shown and described withreference to exemplary embodiments thereof, the invention is not limitedto those specific exemplary embodiments. It will be understood by thoseof ordinary skill in the art that various changes in form and detailsmay be made therein without departing from the spirit and scope of thepresent invention.

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2009-269484, filed on Nov. 27, 2009, thedisclosure of which is incorporated herein in its entirety by reference.

INDUSTRIAL APPLICABILITY

As described above, the present invention is useful as a reputationanalysis system which can appropriately determine change with time inreputation of a subject indicated by a keyword.

DESCRIPTION OF THE REFERENCE NUMERALS

1 CPU

2 communication IF

3 memory

4 HDD

5 input device

6 output device

7 bus

10 voluntary action description extraction unit

20 dictionary of voluntary action expressions

30 document set database

40 reputation time-series data estimation unit

50 reputation change determination unit

100 reputation analysis system according to a first exemplary embodiment

200 reputation analysis system according to a fourth exemplaryembodiment

400 Non-transitory computer-readable recording medium

The invention claimed is:
 1. A reputation analysis system comprising:voluntary action description extraction unit, implemented by a hardwareprocessor, which extracts, from a plurality of documents, a descriptionrepresenting a voluntary action relating to a subject indicated by aninput keyword; a dictionary of voluntary action expressions for storingexpressions expressing voluntary actions relating to a subject, andreputation time-series data estimation unit, implemented by the hardwareprocessor, which counts a number of appearances of voluntary actions foreach point of time when the voluntary action expressed by thedescription representing the voluntary action relating to said subjectis performed, and thereby estimating reputation time-series data whichrepresents evaluations of said subject by agents of the voluntaryactions in a form of a time series, wherein said voluntary actiondescription extraction unit extracts, using said dictionary of voluntaryaction expressions, the description including said keyword and anexpression stored in said dictionary of voluntary action expressions,from said plurality of documents.
 2. The reputation analysis systemaccording to claim 1, wherein said reputation time-series dataestimation unit estimates a point of time when the voluntary action isperformed, using at least either of time information representing a timeof creation or a time of dispatching of said document and the expressionrelating to time in the description representing the voluntary action.3. The reputation analysis system according to claim 1, wherein saiddictionary of voluntary action expressions stores, in addition to theexpressions expressing voluntary actions relating to the subject,evaluation types of evaluations of said subject by agents of actionswhich is estimated from the voluntary actions expressed by saidexpressions.
 4. The reputation analysis system according to claim 3,wherein said reputation time-series data estimation unit estimates saidreputation time-series data on a basis of vector values which arecalculated by counting, in terms of each of the evaluation types, thenumber of appearances of evaluations of said subject by agents ofactions estimated from said expressions expressing voluntary actions. 5.The reputation analysis system according to claim 1, wherein saidvoluntary action description extraction unit extracts a voluntary actiondescription according to a regulation which prescribes positional orgrammatical relations between said keyword and said expressionsexpressing voluntary actions.
 6. The reputation analysis systemaccording to claim 1, further comprising reputation change determinationunit which determines change with time in reputation of said subject onthe basis of an estimation result by said reputation time-series dataestimation unit.
 7. The reputation analysis system according to claim 6,wherein said reputation change determination unit outputs the estimatedsaid change with time in reputation as visualized data.
 8. A reputationanalysis method by a computer including a dictionary of voluntary actionexpressions for storing expressions expressing voluntary actionsrelating to a subject, comprising: extracting, by the computer, from aplurality of documents, a description representing a voluntary actionrelating to a subject indicated by an input keyword and extracting,using said dictionary of voluntary action expressions, the descriptionincluding said keyword and an expression stored in said dictionary ofvoluntary action expressions, from said plurality of documents; andcounting, by the computer, a number of appearances of voluntary actionsfor each point of time when the voluntary action expressed by thedescription representing the voluntary action relating to said subjectis performed, and thereby estimating, by the computer, reputationtime-series data which represents evaluations of said subject by agentsof the voluntary actions in the form of a time series.
 9. Anon-transitory computer-readable program recording medium storing aprogram for reputation analysis for enabling a computer including adictionary of voluntary action expressions for storing expressionsexpressing voluntary actions relating to a subject to execute theprocesses of: extracting, from a plurality of documents, a descriptionrepresenting a voluntary action relating to a subject indicated by aninput keyword; extracting, using said dictionary of voluntary actionexpressions, the description including said keyword and an expressionstored in said dictionary of voluntary action expressions, from saidplurality of documents; and counting a number of appearances ofvoluntary actions for each point of time when the voluntary actionexpressed by the description representing the voluntary action relatingto said subject is performed, and thereby estimating reputationtime-series data which represents evaluations of said subject by agentsof the voluntary actions in the form of a time series.
 10. A reputationanalysis system comprising: a dictionary of voluntary action expressionsfor storing expressions expressing voluntary actions relating to asubject; voluntary action description extraction means for extracting,from a plurality of documents, a description representing a voluntaryaction relating to a subject indicated by an input keyword andextracting, using said dictionary of voluntary action expressions, thedescription including said keyword and an expression stored in saiddictionary of voluntary action expressions, from said plurality ofdocuments; and reputation time-series data estimation means for countinga number of appearances of voluntary actions for each point of time whenthe voluntary action expressed by the description representing thevoluntary action relating to said subject, and thereby estimatingreputation time-series data which represents evaluations of said subjectby agents of the voluntary actions in the form of a time series.